Understanding Native Advertising

We hear a lot about natives, particularly native advertising. In this blog post, let's try to noodle about native advertising, native commerce and native apps. The topic of native advertising has picked up heat early this year after the publication of FTC guidance on native advertising.

What is Native Advertising?


Native advertising is nothing but a mode of showing advertisements matching with the mode and form of underlying platform. For example - promoted tweets that you see in Twitter or suggested products in Amazon or suggested posts in a news website. IAB has defined six standard ad units under the purview of native advertising. They are -

In-feed units 

- these are what I consider the real native ads. They almost look like normal content in the platform; but are actually sponsored ones. For example - twitter promoted tweets.


Paid Search units 

- One of the earliest native ad formats (perhaps even before the term was coined :)). So nothing new here; just that Google or Bing highlights it as 'Sponsored'




Promoted Listings

- Most commonly used in eCommerce sites like Amazon. It is mainly used to showcase related products based on user purchase and browsing behavior.


Recommendation Widgets

- These are similar to promoted listing ad types; but an advertising medium linking text links to external articles/sites. Actually there a set of content marketing/display vendors who play in this domain. Examples include Outbrain, Taboola etc.



In-Ad with native element units

- these are similar to display banner units; but gels well with the content/platform. Once again content marketing/promotion platforms play in this; for example - OneSpot

What makes this domain interesting is how much we can keep the creativity intact. For example, an in-feed ad unit can be designed according to the platform, the publisher site or the app. This brings to the last type - custom ones. For example, the ads shown in Flipboard.

Now that we discussed about what native advertising is and various native ad types; let's shift our focus to one of the important reasons of this topic's uptick -- mobile. We all know content consumption in mobile devices is increasing; and research shows that consumers don’t like traditional display advertising mimicked in mobile (in fact most find it obtrusive). Native mobile ads help in improving the engagement and acceptability of ads in mobile devices.


What are native mobile ads?


Native advertising takes it true format when it comes to mobile. Native mobile ads are those which looks alike in form and format similar to the app or site in which the ad is shown. Since mobile apps provide a variety of capabilities in terms of format,look & feel and access to mobile hardware, native ad formats largely fall in custom one - it can be in-feed, in-game or in-maps etc. - all that matters is creatively include the ad units aligning with your app. Platforms like mopub, inmobi or mobfox are used to develop and show these ads.



If you are interested in seeing live examples from some of the publishers/platforms showing native ads; visit Sharetrough's Native Ad Generator (http://native-generator.sharethrough.com/)

What is Native commerce?


Finally, let's touch on an adjacent topic to native advertising - native commerce as well. The idea is linking eCommerce and content together seamlessly. Similar to in-feed ad units, eCommerce buy links are included in a format aligning with the parent app. So for example, a blog about travel and tourism may show some of the travel packages as ads. This is the marriage of content marketing and eCommerce.

Understanding Data Management Platforms (DMP) aka Data Brokers

Data Management Platforms (DMP) are a set of SaaS offering in the digital advertising ecosystem that have been living for may be around five years now. DMPs have been gaining market noise and Forrester Wave recently published a wave report also.  It's easy to get confused with Tag ManagementSystems that we discussed previously. I believe this post will give a basic understanding of what DMPs are and how they differ from TMS. A DMP's function is three fold - first to  aggregate various data sources, then to integrate them to provide analytics and segmentation capabilities; and finally to deploy actionable insights for various vendors - be it advertising or user experience. 



Let's try to dig a bit deeper into each of these capabilities --

Aggregate data sources


Aggregation of various data sources is the first step that any DMP starts with. Data sources include both online - be it site side analytics, ad serving or optimization and offline - be it transactional data or call center data. These days, all leading DMPs allow collection of data from literally all possible online sources and custom offline integrations.

Integration of data sources


This is the core offering of any DMP. Once the various data sources are integrated, DMPs try to draw conclusions about the profiles of data captured and also provide opportunities to integrate with 3rd party audience segmentation sources like Nielsen. This integration allows Data Management Platforms to provide normalization and segmentation information based on the data accrued. Analysis is done on two fold - based on the first party data that the publisher accrue and then getting input from wider third party data. Let's see a simple example of how this is done.

  1. You make a purchase from a leading e-commerce website. To make purchase, you have given some basic personal information to the company
  1. You visit the brick and mortar store of the company to make a further purchase or return something. Now the company can stitch between your online and offline identities
  1. Based on the information we have, you may be classified into a profile like Male between age group 25 and 35, residing in Kerala state interested in value for money smart phones
  1. You see a display ad showcasing various offers for the smart phone model's accessories; but you didn't click it. However DMP captures the impression
  1. You see a 10% discount ad on an accessory and clicks it, but didn't make the purchase. DMP captures the profile as Male between age group 25 and 35, residing in Kerala state interested in value for money smart phones interested in headsets
  1. Since DMPs create individual profiles, an integration with DSP enables the company to display targeted customized sequential ads for you
  1. You are shown an irresistible offer on the headset, you click and make a purchase
  1. Based on these individual engagement tracking and probabilistic modeling, DMPs create further audience segments
  1. This analysis is fed back into the system for enhancing the segmentation algorithm

Don't believe  this will  all work? Check Bluekai Registry to see under which all segments you belong and who all are tracking you. Of course Personally Identifiable Information(PII) may not be shared by the publisher.

Deployment of actionable insights


Once audience profiles and other insights are developed, DMPs allow it to be directly passed on to a DSP or other online channels. Independent DMPs allow buying actions to be linked to multiple DSPs. It's not just limited to media buying; one can integrate with other marketing channels, for example like  Kenshoo for Paid Search.

DMPs can be pure-play vendors like Bluekai (Oracle) or Lotame; they can part of solution, for example Turn which DMP/DSP integrated or part of a wider marketing cloud like Adobe; or it can be in-house built. DMPs  make sense to those organizations which has built first party data over time. Using the first party data collected as the foundation, DMPs allow accurate customer identities across platforms/devices and optimize media.

Understanding Google RankBrain

A discussion on Google algorithms never get to and end :) We have been discussing about Google and search engine algorithms in general now in a series of posts. While Accelerated Mobile Pages got traction in the last few months; there was also another interesting update from Google on another algorithm component called RankBrain. RankBrain is not an algorithm update in itself; its more a component of a wider algorithm that we discussed earlier - Understanding Google Hummingbird.

What is RankBrain?

We know that Hummingbird algorithm aims to change Google from a search query/result engine to a knowledge engine. Rank Brain could be considered as the major part of the algorithm that helps Google to achieve its goal. Rank Brain is the machine learning/artificial intelligence component embedded to capture the semantics better and further perhaps ingest information into features like Google Knowledge Graph. I think this is one component, which SEOs won't be able to go behind directly (except utilizing things like schemas and mark ups)


RankBrain - One of the most important ranking signal

RankBrain is considered to be among the top three ranking signals Google use. There are supposed to be more than 200 ranking signals used by Google. Rank Brain's machine learning code is expected to connect between ambiguous and vaguely connected queries to provide meaningful results/answers. Over-time it is expected to build a memory of its learning within the algorithm. Thus it is expected to understand the intend behind a search query.

Examples of RankBrain

While a clear and evident example of how RankBrain is impacting search queries; its believed is addressing vague long tail queries. One evident way to see the benefit of this is in usage of various semantic combinations of words in a query. See below for an example -


So essentially Rank Brain is just another factor to make searches and search results smarter! If you would like to get a more in-depth understanding of what rank brain is and what it isn't; do check out Moz's article here.

Here are the other blog posts in which we have discussed search engine ranking algorithms and updates -